MRI radiomics predicts the efficacy of EGFR-TKI in EGFR-mutant non-small-cell lung cancer with brain metastasis

AIM

To develop and validate models based on magnetic resonance imaging (MRI) radiomics for predicting the efficacy of epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) in EGFR-mutant non-small-cell lung cancer (NSCLC) patients with brain metastases.

MATERIALS AND METHODS

117 EGFR-mutant NSCLC patients with brain metastases who received EGFR-TKI treatment were included in this study from January 1, 2014 to December 31, 2021. Patients were randomly divided into training and validation cohorts in a ratio of 2:1. Radiomics features extracted from brain MRI were screened by least absolute shrinkage and selection operator (LASSO) algorithm. Logistic regression analysis and Cox proportional hazard regression analysis were used to screen clinical risk factors. Clinical (C), radiomics (R), and combined (C + R) nomograms were constructed in models predicting short-term efficacy and intracranial progression-free survival (iPFS), respectively. Calibration curves, Harrell's concordance index (C-index), and decision curve analysis (DCA) were used to evaluate the performance of models.

RESULTS

Overall response rate (ORR) was 57.3% and median iPFS was 12.67 months. The C + R nomograms were more effective. In the short-term efficacy model, the C-indexes of C + R nomograms in training cohort and validation cohort were 0.860 (0.820-0.901, 95%CI) and 0.843 (0.783-0.904, 95%CI). In iPFS model, the C-indexes of C + R nomograms in training cohort and validation cohort were 0.837 (0.751-0.923, 95%CI) and 0.850 (0.763-0.937, 95%CI).

CONCLUSION

The C + R nomograms were more effective in predicting EGFR-TKI efficacy of EGFR-mutant NSCLC patients with brain metastases than single clinical or radiomics nomograms.

Enhancing intersection safety in autonomous traffic: A grid-based approach with risk quantification

Existing studies on autonomous intersection management (AIM) primarily focus on traffic efficiency, often overlooking the overall intersection safety, where conflict separation is simplified and traffic conflicts are inadequately assessed. In this paper, we introduce a calculation method for the grid-based Post Encroachment Time (PET) and the total kinetic energy change before and after collisions. The improved grid-based PET metric provides a more accurate estimation of collision probability, and the total kinetic energy change serves as a precise measure of collision severity. Consequently, we establish the Grid-Based Conflict Index (GBCI) to systematically quantify collision risks between vehicles at an autonomous intersection. Then, we propose a traffic-safety-based AIM model aimed at minimizing the weighted sum of total delay and conflict risk at the intersection. This entails the optimization of entry time and trajectory for each vehicle within the intersection, achieving traffic control that prioritizes overall intersection safety. Our results demonstrate that GBCI effectively assesses conflict risks within the intersection, and the proposed AIM model significantly reduces conflict risks between vehicles and enhances traffic safety while ensuring intersection efficiency.

Pharmacological Research Progress of Novel Antihypertensive Drugs

Cardiovascular disease stands as the leading cause of death globally, with hypertension emerging as an independent risk factor for its development. The worldwide prevalence of hypertension hovers around 30%, encompassing a staggering 1.2 billion patients, and continues to escalate annually. Medication plays a pivotal role in managing hypertension, not only effectively regulating blood pressure (BP) but also substantially mitigating the occurrence of cardiovascular and cerebrovascular diseases. This review comprehensively outlines the categories, mechanisms, clinical applications, and drawbacks of conventional antihypertensive drugs. It delves into the five primary pharmacological classifications, namely β-receptor blockers, calcium channel blockers (CCBs), angiotensin-converting enzyme inhibitors (ACEIs), angiotensin receptor blockers (ARBs), and diuretics. The emphasis is placed on elucidating the mechanisms, advantages, and research progress of novel antihypertensive drugs targeting emerging areas. These include mineralocorticoid receptor antagonists (MRAs), atrial natriuretic peptides (ANPs), neutral endopeptidase inhibitors (NEPIs), sodium-dependent glucose transporter 2 inhibitors (SGLT-2Is), glucagon-like peptide-1 receptor agonists (GLP-1RAs), endothelin receptor antagonists (ERAs), soluble guanylate cyclase (sGC) agonists, brain aminopeptidase A inhibitors (APAIs), and small interfering ribonucleic acids (siRNAs) targeting hepatic angiotensinogen. Compared to conventional antihypertensive drugs, these novel alternatives exhibit favorable antihypertensive effects with minimal adverse reactions. This review serves as a valuable reference for future research and the clinical application of antihypertensive drugs.

The research of river basin ecological compensation based on water emissions trading mechanism

By integrating the successful case of the European Union emissions trading system, this study proposes a water emissions trading system, a novel method of reducing water pollution. Assuming that upstream governments allocate initial quotas to upstream businesses as the compensation standard, this approach defines the foundational principles of market trading mechanisms and establishes a robust watershed ecological compensation model to address challenges in water pollution prevention. To be specific, the government establishes a reasonable initial quota for upstream enterprises, which can be used to limit the emissions of upstream pollution. When enterprises exceed their allocated emissions quota, they face financial penalties. Conversely, these emissions rights can be transformed into profitable assets by participating in the trading market as a form of ecological compensation. Numerical simulations demonstrate that various pollutant emissions from upstream businesses will have various effects on the profits of other businesses. Businesses in the upstream region received reimbursement from the assigned emission rights through the market mechanism, demonstrating that ecological compensation for the watershed can be achieved through the market mechanism. This novel market trading system aims at controlling emissions management from the perspectives of individual enterprises and ultimately optimizing the aquatic environment.

Effects of intermittent cold stimulation on growth performance, meat quality, antioxidant capacity and liver lipid metabolism in broiler chickens

Intermittent cold stimulation (ICS) enhances broilers' resistance to cold stress. Nonetheless, further research is needed to investigate the underlying mechanisms that enhance cold stress resistance. A total of 160 one-day-old male Ross 308 broilers were randomly divided into 2 groups (CC and CS5), with the CC group managing temperature according to the standard for broiler growth stages, while the CS5 group were subjected to cold stimulation at a temperature 3℃ lower than the CC group for 5 h, every 2 d from 15 to 35 d. Sampling was conducted at 36 d (36D), 50 d (50D) and after acute cold stress for 24 h (Y24). First, we examined the effects of ICS on broiler growth performance, meat quality, antioxidant capacity, and lipid metabolism. The results demonstrated that ICS enhanced the performance of broilers to a certain degree. Specifically, the average weight gain in the CS5 group was significantly higher than that of the CC group, and the feed conversion ratio significantly decreased compared to CC at 4 W and 6 W (P ≤ 0.05). Compared with the CC group, cold stimulation significantly reduced drip loss, shearing force, and yellowness (a* value) of chicken meat, while significantly increased redness (b* value) (P ≤ 0.05). At Y24, the levels of T-AOC and GSH-PX in the serum of the CS5 group were significantly higher than those of the CC group, while the level of MDA was significantly lower (P ≤ 0.05). The content of TG, FFA, and VLDL in the serum of the CS5 group was significantly elevated, whereas the level of TC and HDL was significantly lower (P ≤ 0.05). In addition, we further explored whether AMPK-mTOR pathway is involved in the regulation of changes in lipid metabolism and the possible regulatory mechanisms downstream of the signaling pathway. The results showed that ICS significantly upregulated the expression levels of AMPK mRNA and protein in the liver of the CS5 group at 36D and Y24, while significantly down-regulating mTOR (P ≤ 0.05). Compared with the CC group, ICS significantly down-regulated the mRNA expression levels of lipid synthesis and endoplasmic reticulum stress-related genes (SREBP1c, FAS, SCD, ACC, GRP78 and PERK) at 36D and Y24, while significantly up-regulating the mRNA expression levels of lipid decomposition and autophagy-related genes (PPAR and LC3) (P ≤ 0.05). In addition, at Y24, the protein expression levels of endoplasmic reticulum stress-related genes (GRP78) in the CS5 group were significantly lower, while autophagy-related genes (LC3 and ATG7) were significantly higher (P ≤ 0.05). ICS can affect meat quality and lipid metabolism in broilers, and when broilers are subjected to acute cold stress, broilers trained with cold stimulation have stronger lipid metabolism capacity.

Gold(I) ion and the phosphine ligand are necessary for the anti-Toxoplasma gondii activity of auranofin

Auranofin, an FDA-approved drug for rheumatoid arthritis, has emerged as a promising antiparasitic medication in recent years. The gold(I) ion in auranofin is postulated to be responsible for its antiparasitic activity. Notably, aurothiomalate and aurothioglucose also contain gold(I), and, like auranofin, they were previously used to treat rheumatoid arthritis. Whether they have antiparasitic activity remains to be elucidated. Herein, we demonstrated that auranofin and similar derivatives, but not aurothiomalate and aurothioglucose, inhibited the growth of Toxoplasma gondii in vitro. We found that auranofin affected the T. gondii biological cycle (lytic cycle) by inhibiting T. gondii's invasion and triggering its egress from the host cell. However, auranofin could not prevent parasite replication once T. gondii resided within the host. Auranofin treatment induced apoptosis in T. gondii parasites, as demonstrated by its reduced size and elevated phosphatidylserine externalization (PS). Notably, the gold from auranofin enters the cytoplasm of T. gondii, as demonstrated by scanning transmission electron microscopy-energy dispersive X-ray spectroscopy (STEM-EDS) and Inductively Coupled Plasma-Mass Spectrometry (ICP-MS).IMPORTANCEToxoplasmosis, caused by Toxoplasma gondii, is a devastating disease affecting the brain and the eyes, frequently affecting immunocompromised individuals. Approximately 60 million people in the United States are already infected with T. gondii, representing a population at-risk of developing toxoplasmosis. Recent advances in treating cancer, autoimmune diseases, and organ transplants have contributed to this at-risk population's exponential growth. Paradoxically, treatments for toxoplasmosis have remained the same for more than 60 years, relying on medications well-known for their bone marrow toxicity and allergic reactions. Discovering new therapies is a priority, and repurposing FDA-approved drugs is an alternative approach to speed up drug discovery. Herein, we report the effect of auranofin, an FDA-approved drug, on the biological cycle of T. gondii and how both the phosphine ligand and the gold molecule determine the anti-parasitic activity of auranofin and other gold compounds. Our studies would contribute to the pipeline of candidate anti-T. gondii agents.

A chitosan-based conductive double network hydrogel doped by tannic acid-reduced graphene oxide with excellent stretchability and high sensitivity for wearable strain sensors

Conductive hydrogels usually suffer from weak mechanical properties and are easily destroyed, resulting in limited applications in flexible electronics. Concurrently, adding conductive additives to the hydrogel solution increases the probability of agglomeration and uneven dispersion issues. In this study, the biocompatible natural polymer chitosan was used as the network substrate. The rigid network employed was the Cit3-ion crosslinked chitosan (CS) network, and the MBA chemically crosslinked polyacrylamide (PAM) network was used as the flexible network. Tannic acid-reduced graphene oxide (TA-rGO), which has excellent conductivity and dispersibility, is used as a conductive filler. Thus, a CS/TA-rGO/PAM double network conductive hydrogel with excellent performance, high toughness, high conductivity, and superior sensing sensitivity was prepared. The prepared CS/TA-rGO/PAM double network conductive hydrogels have strong tensile properties (strain and toughness as high as 2009 % and 1045 kJ/cm3), excellent sensing sensitivity (GF value was 4.01), a wider strain detection range, high cycling stability and durability, good biocompatibility, and antimicrobial properties. The hydrogel can be assembled into flexible wearable devices that can not only dynamically detect human movements, such as joint bending, facial expression changes, swallowing, and saying, but also recognize handwriting and enable human-computer interaction.

Spatio-temporal effects of digital inclusive finance on the synergy between CO2 and air pollution emissions in 251 Chinese cities

Achieving the synergistic reduction of CO2 and air pollution emissions (SRCAPEs) holds great significance in promoting the green transformation. However, limited research has been conducted on the spatio-temporal impact of digital inclusive finance (DIF) on the synergy between CO2 and air pollution emissions (SCAPEs). To address this gap, we comprehensively employ the linear regression model, geographically and the temporally weighted regression (GTWR) model, and the ordered probit model to empirically analyze the influence of DIF on SCAPE. Our research reveals the following: (1) The linear regression model demonstrates that, on average, DIF can achieve a weak synergistic emission reduction effect. This result remains robust after a battery of robustness tests. (2) The GTWR model reveals that the impact of DIF on both emissions exhibits evident spatio-temporal characteristics. Its emission reduction effect gradually increases, especially after 2014. (3) On the basis of the estimates from the GTWR model, we can identify four distinct synergy types driven by DIF. The number of cities with the preferred type (i.e., achieving SRCAPE) increases the most, from 59 in 2011 to 233 in 2019. (4) On the basis of the built ordered probit models, green technology innovation is an important path for DIF to achieve synergistic emission reduction. The synergistic emission reduction effect is also significantly moderated by the regional economic level and environmental regulation intensity. Our findings have policy implications for central and local governments in achieving SRCAPE and support efforts to achieve sustainable development.

Research progress of the detection and analysis methods of heavy metals in plants

Heavy metal (HM)-induced stress can lead to the enrichment of HMs in plants thereby threatening people's lives and health via the food chain. For this reason, there is an urgent need for some reliable and practical techniques to detect and analyze the absorption, distribution, accumulation, chemical form, and transport of HMs in plants for reducing or regulating HM content. Not only does it help to explore the mechanism of plant HM response, but it also holds significant importance for cultivating plants with low levels of HMs. Even though this field has garnered significant attention recently, only minority researchers have systematically summarized the different methods of analysis. This paper outlines the detection and analysis techniques applied in recent years for determining HM concentration in plants, such as inductively coupled plasma mass spectrometry (ICP-MS), atomic absorption spectrometry (AAS), atomic fluorescence spectrometry (AFS), X-ray absorption spectroscopy (XAS), X-ray fluorescence spectrometry (XRF), laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS), non-invasive micro-test technology (NMT) and omics and molecular biology approaches. They can detect the chemical forms, spatial distribution, uptake and transport of HMs in plants. For this paper, the principles behind these techniques are clarified, their advantages and disadvantages are highlighted, their applications are explored, and guidance for selecting the appropriate methods to study HMs in plants is provided for later research. It is also expected to promote the innovation and development of HM-detection technologies and offer ideas for future research concerning HM accumulation in plants.

Remnant cholesterol is associated with hip BMD and low bone mass in young and middle-aged men: a cross-sectional study

PURPOSE

Remnant cholesterol (RC) is a contributor to cardiovascular diseases, obesity, diabetes, and metabolic syndrome. However, the specific relationship between RC and bone metabolism remains unexplored. Therefore, we aimed to investigate the relationships of RC with hip bone mineral density (BMD) and the risk of low bone mass.

METHODS

Physical examination data was collected from men aged < 60 years as part of the Kailuan Study between 2014 and 2018. The characteristics of the participants were compared between RC quartile groups. A generalized linear regression model was used to evaluate the relationship between RC and hip BMD and a logistic regression model was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for low bone mass. Additional analyses were performed after stratification by body mass index (BMI) (≥ or < 24 kg/m2). Sensitivity analyses were performed by excluding individuals who were taking lipid-lowering therapy or had cancer, cardiovascular diseases, or diabetes.

RESULTS

Data from a total of 7,053 participants were included in the analysis. After adjustment for confounding factors, RC negatively correlated with hip BMD (β =  - 0.0079, 95% CI: - 0.0133, - 0.0025). The risk of low bone mass increased from the lowest to the highest RC quartile, with ORs of 1 (reference), 1.09 (95% CI: (0.82, 1.44), 1.35 (95%CI: 1.02, 1.77), and 1.43 (95% CI: 1.09, 1.89) for Q1, Q2, Q3, and Q4, respectively (P for trend = 0.004) in the fully adjusted model. Compared to RC < 0.80 mmol/l group, the risk of low bone mass increased 39% in RC ≥ 0.80 mmol/l group (P < 0.001). The correlation between RC and hip BMD was stronger in participants with BMI ≥ 24 kg/m2 group (β =  - 0.0159, 95% CI: - 0.0289, - 0.0029). The results of sensitivity analyses were consistent with the main results.

CONCLUSION

We have identified a negative correlation between serum RC and hip BMD, and a higher RC concentration was found to be associated with a greater risk of low bone mass in young and middle-aged men.

Comparison of different dosages of propofol combined with its equivalent alfentanil in outpatient abortion: a prospective, double-blinded, randomized trial

OBJECTIVE

This study aimed at determining the optimal dose combination of alfentanil and propofol for outpatient abortion anesthesia.

PATIENTS AND METHODS

The study was separated into two parts. In the first part, patients were to determine the median effective dose (ED50) and the 95% effective dose (ED95) of alfentanil in combination with 2.5 mg·kg-1 propofol to inhibit body movements during the abortion using the Dixon up-and-down sequential allocation method. In the second part, 170 patients were randomly divided into group C (2.0 mg·kg-1 propofol with alfentanil 12.16 μg·kg-1) and group E (2.5 mg·kg-1 propofol with its ED95) to compare the anesthetic effect. The primary outcome was the sedation level during general anesthesia. The secondary outcomes were circulation, respiratory complications, and postoperative recovery quality.

RESULTS

The ED50 and the ED95 values of alfentanil were 3.37 μg·kg-1 (95% CI: 2.58-3.97 μg·kg-1) and 4.68 μg·kg-1 (95% CI: 4.04-9.32 μg·kg-1). The frequency of deep sedation in group E was significantly higher than in group C (76.5% vs. 60%). Patients in group C showed more wakefulness even during the surgery (14.3% vs. 4.4%). The results of our exploratory analyses did not reveal differences in respiratory depression, circulatory depression, postoperative side effects, or recovery outcomes.

CONCLUSIONS

The combination of 2.5 mg·kg-1 propofol and 4.68 μg·kg-1 alfentanil produces a better sedative effect than the combination of 2.0 mg·kg-1 propofol and 12.16 μg·kg-1 alfentanil without increasing additional risks associated with anesthesia.

Intermittent mild cold stimulation alleviates cold stress-induced pulmonary fibrosis by inhibiting the TGF-β1/Smad signaling pathway in broilers

To investigate the potential protective effect of intermittent cold stimulation on lung tissues of broilers exposed to acute cold stress (ACS). A total of 384 one-day-old broilers were assigned to 4 experimental groups with 6 replicates of 16 birds each: control (CON) and ACS groups were reared at normal feeding temperature from d 1 to 42; cold treatment groups (CS3+ACS and CS9+ACS) were reared, respectively, at 3°C or 9°C for 5 h on alternate days below the CON group from d 15 to 35. Animals in CS3+ACS, CS9+ACS, and ACS groups were exposed at 10°C for 24 h on d 43. Subsequently, lung tissues were collected to perform histopathological examination and measurement of relevant indexes. The results showed that lung tissues in CS9+ACS and ACS groups exhibited increased inflammatory cell infiltrates and collagen deposition compared to the CON group, while this pathological phenomenon was less pronounced in the CS3+ACS group. Compared to CON group, H2O2 and MDA contents were increased, and the activities of antioxidant enzymes (CAT, SOD, GPx, T-AOC) were reduced in CS9+ACS and ACS group (P < 0.05); mRNA and protein levels of inhibitor of NF-κB, Smad7, matrix metallopeptidase (MMP)-2, MMP9, and antioxidant-related genes were downregulated, whereas mRNA and protein levels of genes related to NF-κB/NLRP3 pathway-regulated inflammation and TGF-β1/Smad pathway-regulated fibrosis were upregulated in cold-stressed broilers (P < 0.05). mRNA levels of heme oxygenase-1, NAD(P)H quinone oxidoreductase-1, and MMP9 were increased in CS3+ACS group (P < 0.05). Moreover, the expression of most antioxidant-related genes was increased, and that of inflammation- and fibrosis-related genes was reduced in CS3+ACS group (P < 0.05). Therefore, cold stress caused oxidative stress and inflammation, leading to pulmonary fibrosis in broilers, whereas intermittent mild cold stimulation at 3°C below normal rearing temperature alleviated fibrosis by inhibiting the TGF-β1/Smad pathway modulated by the Nrf2/HO-1 and NF-κB/NLRP3 signaling pathway. This study suggests that intermittent mild cold stimulation can be a potential strategy to reduce ACS-induced lung damage in broilers.

Intermittent cold stimulation affects energy metabolism and improves stress resistance in broiler heart

To investigate the effect of intermittent cold stimulation on cardiac energy metabolism and cold resistance of broilers, 288 broilers were divided into 3 groups: control group (CC) and 2 cold stimulation groups (CS3 and CS9). The CS3 and CS9 groups received cold stimulation at temperatures of 3°C and 9°C lower than CC group for 5 h from d 15 to 35. Three groups were subjected to acute cold stress (ACS) of 10°C for 12 and 24 h at 44 d. Performance, cardiac histopathological changes, heat shock proteins (HSPs), and lipid metabolism levels were measured. Results showed that the performance was not different among groups at 22 and 29 d (P > 0.05), but the mRNA levels of Acyl CoA synthase long-chain family member 1 (ACSL1) and acyl-coenzyme oxidase (ACO) in CS group were upregulated compared to CC group (P < 0.05). At 36 d, the performance of the CS3 group was better than the other 2 groups, myocardial structure was normal and other lipid metabolism indexes, except for peroxisome proliferator-activated receptor coactivator 1α (PGC-1α) levels, were similar to those of CC group (P > 0.05). The myocardial fiber disorder, Triglyceride (TG), and leptin (LEP) contents were significantly lower in CS9 group than in CC and CS3 groups at 36 d (P < 0.05). The HSP protein levels were significantly higher in CS group than in CC group before ACS (P < 0.05). After 24 h of ACS, the mRNA of lipid metabolism genes, the protein levels of HSP40 and HSP60, and the contents of TG and LEP in the CS3 group were upregulated compared to other groups. The CC and CS9 groups showed myocardial structure was destroyed, with lower TG and LEP levels compared to before ACS (P < 0.05). Therefore, cold stimulation at 3°C lower than the normal feeding temperature for 5 h did not impair performance but can increase the resistance of broilers to ACS by promoting lipid metabolism.

Mild Intermittent Cold Stimulation Affects Cardiac Substance Metabolism via the Neuroendocrine Pathway in Broilers

This study aimed to investigate the impact of cold adaptation on the neuroendocrine and cardiac substance metabolism pathways in broilers. The broilers were divided into the control group (CC), cold adaptation group (C3), and cold-stressed group (C9), and experimental period was divided into the training period (d 1-35), recovery period (d 36-43), and cold stress period (d 43-44). During the training period, the CC group was reared at ambient temperature, while C3 and C9 groups were reared at 3 °C and 9 °C lower than the ambient temperature, respectively, for 5 h/d at 1 d intervals. During the recovery period, all the groups were maintained at 20 °C. Lastly, during the cold stress period, the groups were divided into two sub-groups, and each sub-group was placed at 10 °C for 12 h (Y12) or 24 h (Y24) for acute cold stimulation. The blood, hypothalamic, and cardiac tissues samples were obtained from all the groups during the training, recovery, and acute stress periods. The results revealed that the transcription of calcium voltage-gated channel subunit alpha 1 C (CACNAIC) was increased in the hypothalamic tissues of the C3 group (p < 0.05). Moreover, compared to the CC group, the serum norepinephrine (NE) was increased in the C9 group (p < 0.05), but insulin (INS) was decreased in the C9 group (p < 0.05). In addition, the transcription of the phosphoinositide-3 kinase (PI3K), protein kinase B (Akt), mammalian target of rapamycin (mTOR), SREBP1c, FASN, ACC1, and SCD genes was down-regulated in the C3 and C9 groups (p < 0.05); however, their expression increased in the C3 and C9 groups after acute cold stimulation (p < 0.05). Compared to the CC group, the transcription of forkhead box O1 (FoxO1), PEPCK, G6Pase, GLUT1, HK1, PFK, and LDHB genes was up-regulated in the C3 and C9 groups (p < 0.05. Furthermore, compared to the CC and C9 groups, the protein and mRNA expressions of heat shock protein (HSP) 70 and HSP90 were significantly increased in the C3 group (p < 0.05). These results indicate that intermittent cold training can enhance cold stress tolerance in broilers by regulating their neuroendocrine and cardiac substance metabolism pathways.

Government digital policy breaks the mystery of "limited participation" in China's home finance market

This study uses a two-step approach to construct a multi-period double-difference model and introduces a quasi-natural experiment of the Broadband China pilot policy to investigate whether household financial market participation at the urban level is affected by the digital economy, which is significant for promoting Chinese households' shift from savings to investment and alleviating the long-standing problem of insufficient household financial market participation in China. In terms of direct impact, the digital economy increases the household financial market participation rate of urban residents by 3.26%, and increases the financial market participation rate of highly financially literate households by 2.14%; in terms of indirect impact, the development of the digital economy increases the total number of household smart Internet devices by 8.27%, and similarly increases the attention to household financial information by a significant 4.22%, which further positively influences the household financial market participation rate. This paper also evaluates the individual and regional differences of the digital economy on household financial market participation, and the estimated causal effect of the digital economy on household financial market participation is purer, which expands the scope of research on the digital economy and household financial market participation, and provides a certain reference basis and policy inspiration for the government to promote the construction of the digital economy.

Exosomes Derived from M2 Microglial Cells Modulated by 1070-nm Light Improve Cognition in an Alzheimer's Disease Mouse Model

Near-infrared photobiomodulation has been identified as a potential strategy for Alzheimer's disease (AD). However, the mechanisms underlying this therapeutic effect remain poorly characterize. Herein, it is illustrate that 1070-nm light induces the morphological alteration of microglia from an M1 to M2 phenotype that secretes exosomes, which alleviates the β-amyloid burden to improve cognitive function by ameliorating neuroinflammation and promoting neuronal dendritic spine plasticity. The results show that 4 J cm-2 1070-nm light at a 10-Hz frequency prompts microglia with an M1 inflammatory type to switch to an M2 anti-inflammatory type. This induces secretion of M2 microglial-derived exosomes containing miR-7670-3p, which targets activating transcription factor 6 (ATF6) during endoplasmic reticulum (ER) stress. Moreover, it is found that miR-7670-3p reduces ATF6 expression to further ameliorate ER stress, thus attenuating the inflammatory response and protecting dendritic spine integrity of neurons in the cortex and hippocampus of 5xFAD mice, ultimately leading to improvements in cognitive function. This study highlights the critical role of exosomes derive from 1070-nm light-modulated microglia in treating AD mice, which may provide a theoretical basis for the treatment of AD with the use of near-infrared photobiomodulation.

Crash injury severity prediction considering data imbalance: A Wasserstein generative adversarial network with gradient penalty approach

For each road crash event, it is necessary to predict its injury severity. However, predicting crash injury severity with the imbalanced data frequently results in ineffective classifier. Due to the rarity of severe injuries in road traffic crashes, the crash data is extremely imbalanced among injury severity classes, making it challenging to the training of prediction models. To achieve interclass balance, it is possible to generate certain minority class samples using data augmentation techniques. Aiming to address the imbalance issue of crash injury severity data, this study applies a novel deep learning method, the Wasserstein generative adversarial network with gradient penalty (WGAN-GP), to investigate a massive amount of crash data, which can generate synthetic injury severity data linked to traffic crashes to rebalance the dataset. To evaluate the effectiveness of the WGAN-GP model, we systematically compare performances of various commonly-used sampling techniques (random under-sampling, random over-sampling, synthetic minority over-sampling technique and adaptive synthetic sampling) with respect to dataset balance and crash injury severity prediction. After rebalancing the dataset, this study categorizes the crash injury severity using logistic regression, multilayer perceptron, random forest, AdaBoost and XGBoost. The AUC, specificity and sensitivity are employed as evaluation indicators to compare the prediction performances. Results demonstrate that sampling techniques can considerably improve the prediction performance of minority classes in an imbalanced dataset, and the combination of XGBoost and WGAN-GP performs best with an AUC of 0.794 and a sensitivity of 0.698. Finally, the interpretability of the model is improved by the explainable machine learning technique SHAP (SHapley Additive exPlanation), allowing for a deeper understanding of the effects of each variable on crash injury severity. Findings of this study shed light on the prediction of crash injury severity with data imbalance using data-driven approaches.

A Self-Attention-Based Neural Network for Predicting Immune Checkpoint Inhibitors Response

PURPOSE/OBJECTIVE(S)

Cancer cells evade immune system by negatively regulating T cells via immune checkpoints (e.g., PD-1). By blocking these checkpoints, the ability of immune system to recognize and kill cancer cells restores. Individual response rate of checkpoint blockade varies among patients, with 50%-80% in specific types of cancer such as melanoma, while only 15%-30% in most other tumors. Yet it is still an open question what is the set of biomarkers that are crucial to the response to immune checkpoint inhibitors (ICI). The overall goal of this study is to develop and validate a biologically-aware interpretable deep learning model to identify the biomarkers that can predict the survival outcome to ICI treatment.

MATERIALS/METHODS

The self-attention mechanism could yield interpretable results where important biomarkers may have more "attention". However, in classical self-attention mechanism, the prior biological knowledge of protein interactions (PPI) and gene pathways are not incorporated. In this study, we propose a weighted biologically-aware attention score, where it is weighted against the gene centrality and pathway length. The genes that are closely connected to mutated genes receive 'high attention', while the genes that are far away from mutated genes along the pathway receive "lower attention". We then train, validate and test our model using 1,660 patients of nine types of cancer. To validate the prediction, 1. We evaluate the accuracy via concordance index. 2. We identified the genes that receive high attention and verify their functions in existed literature. 3. We perform sanity check by removing these genes from the data, re-training and predicting again, and comparing the prediction accuracy.

RESULTS

Our framework has achieved an average accuracy (measured via c-index) of 0.60 ± 0.06 for NSCLC and 0.58 ± 0.07 for melanoma, which is superior to both the gold standard COX-PH model (0.57 ± 0.06 for NSCLC and 0.53 ± 0.03 for melanoma) and DeepSurv (0.54 ± 0.05 for NSCLC and 0.51 ± 0.10 for melanoma). Genes that receive high attention have been validated by supporting literature, which provides an additional means of verifying the prediction in comparison to "black box" deep learning models, where there is no way to comprehend the reason behind predictions. Removing the top 8% high-attention genes (∼25 genes) from the data while using the remaining 92% for making predictions resulted in a drop in accuracy to 0.55 ± 0.073 for NSCLC and 0.56 ± 0.03 for melanoma, underscoring the significance of these genes. Patient stratification is also performed by dividing patients into responders and non-responders based on prediction score.

CONCLUSION

In this study, we propose and validate a biologically-aware self-attention based deep learning model which outperforms commonly-used survival models. Additionally, this tool has the potential to identify key biomarkers while assist in clinical decision-making, which demonstrates a promising step for immunotherapy response prediction.

Accelerating Volumetric CT and MRI Imaging by Reference-Free Deep Learning Transformation from Low-Resolution to High-Resolution

PURPOSE/OBJECTIVE(S)

High-resolution (HR) images are important in precision radiation oncology. However, acquiring HR volumetric CT and MRI images is often time consuming; also, the resolution in some direction(s) (e.g., z-direction in the case of CT) is often limited by imaging hardware or fundamental imaging principle. Super-resolution (SR) imaging, i.e., the low-resolution (LR) to HR image transformation, is widely used to improve image resolution. Data-driven deep learning (DL) methods have achieved great success in SR imaging, yet they can hardly be applied to medical imaging as they require large amount of LR-HR image pairs to train the model. We therefore propose a reference-free DL method to increase resolutions of volumetric medical images in an efficient way.

MATERIALS/METHODS

We propose a maximum likelihood estimation (MLE)-based implicit neural representation (INR) network for SR imaging. The INR network aims to represent an image as a continuous function parameterized by a coordinate-based multi-layer perceptron. The INR network takes image coordinates as input and outputs corresponding pixel intensities. To train the network without using any HR images, we use a MLE framework to model LR observations' statistics and their relation to the latent HR image. The predicted HR image from the INR's output is transformed to LR images based on the MLE, and the network parameters are then optimized by minimizing the distance between the transformed LR images and actual LR observations. We demonstrate the efficacy of the proposed method on CT and MRI images for 2x, 4x, and 8x SR using only one or two LR image(s). The performance is compared with a conventional SR method named plain MLE, in terms of visual quality and numerical qualities of PSNR and SSIM.

RESULTS

Our method outperformed the plain MLE method in the experiment. Table 1 reports the numerical improvements of our method over the compared plain MLE method. For 2x SR with a single LR image, our method achieved significant improvements in both PSNR and SSIM. When using two LR images, the better structural restoration capability of our method became more obvious with higher SR magnifications, as indicated by the increased SSIM differences. Better noise suppression capability of our method is observed in all our studies, as indicated by the PSNR values. In visual quality evaluation, we observed sharper image details with less noise in SR images generated by the proposed method, compared with the plain MLE method.

CONCLUSION

The proposed novel reference-free DL method can efficiently provide high-quality HR images with only one or two LR images for CT and MRI imaging. This method can be easily generalized to many other radiation therapy related applications without the requirement for HR reference images.

Reinforcement Learning Powered Station Parameter Optimized Radiation Therapy (SPORT): A Novel Treatment Planning and Beam Delivery Technique

PURPOSE/OBJECTIVE(S)

Conventional intensity modulated radiation therapy (IMRT) with a typical 5-20 fixed beams often does not provide sufficient angular sampling required for conformal dose shaping, whereas current volumetric modulated arc therapy (VMAT) discretizes the angular space into equally spaced control points without considering the differential need for intensity modulation of different angles, leading to undersampling at some angles while oversampling at some other angles. Our goal is to develop a node or station parameter optimized radiation therapy (SPORT) strategy with simultaneously optimized angular sampling and beam modulation by leveraging state-of-the-art reinforcement learning and the unique capability of modern digital LINACs in dose delivery through programmable nodal points.

MATERIALS/METHODS

We developed a SPORT optimization framework, in which, the process of programming control points (or station parameters) was formulated as a stochastic dynamic programming problem, which was solved by a reinforcement learning-based algorithm. On-policy reinforcement learning method, namely, state-action-reward-state-action (SARSA) was integrated with deep convolutional neural network to predict station parameters by utilizing the patient's anatomical structures meanwhile considering the delivery capability of a typical digital LINAC machine. Here, the deep convolutional neural network estimated the state-action value by using the quality of the plan with current station parameters when a next potential station parameter was selected. The state-action value was then updated by SARSA learning. The quality of the plan was quantified by dosimetry constraints. The model was assessed by a retrospective study on a cohort of patients underwent head-and-neck radiation therapy. Dosimetric analysis and delivery efficiency comparisons were used to evaluate the performance of the proposed framework.

RESULTS

Our model was used to generate 16 plans unseen in the original training set. All the plans predicted by our model achieved better dose distributions without violating clinical planning constraints. Moreover, instead of using 4 full standard arcs in the original clinically used plans obtained via manual optimization, the predicted plans only used one full standard arc (about 178 control points) plus boost from a few sub-arcs (less than 30 degrees of gantry angles), which significantly improved the efficiency of the beam delivery. We are in the process of integrating the sub-arcs into the full arc by considering the programmable capability of modern LINACs.

CONCLUSION

We demonstrated that a machine learning-based SPORT framework capable of optimizing the spatial sampling and beam modulation simultaneously for modern radiation therapy. The framework not only significantly improves the quality and efficiency of beam delivery, but also has the potential to be incorporated into current clinical workflow to improve the efficiency of dose planning and delivery.

Automated Small Tumor Segmentation by a Template-Based Global Hierarchical Attention Method

PURPOSE/OBJECTIVE(S)

Accurate segmentation of tumors is significant for radiation therapy treatment planning and clinical decision-making. While deep convolutional neural network-based methods have found valuable applications in automatic medical segmentation, tumor segmentation, especially small tumor segmentation, remains challenging due to deficiencies of current deep learning in convolutional and pooling operations, which often results in the loss of small object information. This research proposes a global hierarchical attention-based method for accurate and automated segmentation of small tumors by exploiting the associations between small tumors and the feature maps of large tumors.

MATERIALS/METHODS

This study included 131 patients with liver cancer. The in-plane resolution of the patients' CTs is from 0.55 mm to 1.0 mm and slice spacing from 0.45 mm to 6.0 mm. We randomly selected 100 CT scans as the training set and others as the testing set. Each CT slice of the testing set was separated into groups according to tumor size as follows: 0.1-2.0, 2.1-5.0, 5.1-10.0, and 10.1-20.0 cm. The CT slice without tumor or tumor size > 20 cm were excluded. This work presents a tumor template-based hierarchical attention method to quantify the relation between small and large tumors by computing their feature maps. The relation of small-large tumors can compensate for the information loss of small tumors during the convolutional and pooling operations and improve the performance of small tumor segmentation.

RESULTS

Among 20,693 CT slices of the 31 testing patients, 3.0% CT slices with tumors ≤2 cm, 6.7% ≤5 cm, 10.6% ≤10 cm, and 13.4%≤20 cm. We compared our method with six widely used segmentation models. The results show our model outperforms other methods on all sizes of liver tumors, especially for small size tumors: For the 0.1-2.0 cm liver tumor, it achieved 8.4%, 10.0%, 11.3%, 9.1%, 10.9%, and 9.6% improvement compared to Unet, PAN, DeepLabV3, FPN, LinkNet, and PSPNet, respectively.

CONCLUSION

We found that the small-large tumors relation can significantly improve small tumor segmentation, which is valuable for treatment planning, and clinical decision-making. Our experimental results show that our method can significantly improve the accuracy of segmenting small liver tumors compared to existing deep-learning-based models. The method is quite general and can be extended to other types of tumor detection and segmentation. We discovered that the relationship between small and large tumors can significantly enhance the segmentation of small tumors, which has significant value for treatment planning and clinical decision-making. Our experiments demonstrate that our approach significantly improves the accuracy of small liver tumor segmentation compared to existing deep learning-based models. Our method is quite versatile and can be extended to other types of tumor detection and segmentation.

An Efficient Transformer Model for Synthesizing Dual Energy CT from Single Energy Scanner

PURPOSE/OBJECTIVE(S)

Dual-energy CT can be used to optimize radiation treatment. Recently, deep learning has been demonstrated to synthesize high-energy CT images from low-energy ones for dose reduction and lower CT system burden. As the state-of-the-art deep learning architecture, the computation burden of Transformer increases quadratically with the feature size, making the model training resource-demanding or even infeasible. Here, we introduce an efficient transformer for the balance between CT image synthesis quality and computational burden.

MATERIALS/METHODS

The model is a U-shape deep neural network with encoders and decoders built by Transformer blocks. The model input is low-energy 100kVp CT image and the output is high-energy 140kVp one. Each block has a Self Channel Correlation Unit (SCCU) and a Self Spatial Attention Unit (SSAU). Local shortcuts are applied for both units. Under-sampling operation achieved by pixel shuffling is used to obtain multi-scale feature maps, and the transformer block is applied on each feature scale. Symmetric skip connection sending features from shallow layers to deep layers, thus an additional 1 × 1 convolution is used for feature fusion in each decoder. In a SCCU, the feature is first mapped to one Query, one Key, and one Value. Then the Query and the Key tensors perform matrix multiplication to compute cross covariance of feature channels. The channel correlation score can be obtained by normalization of the covariance, and it is used to weight the Value tensor. As a result, the model complexity only increases linearly with the feature size. Besides the channel weighting, we enhance spatial information using SSAU, where the feature is mapped to two tensors. One tensor after activation is used to point-wisely calibrate another tensor. Additional Transformer blocks are cascaded to the last decoder for feature refinement. Because of the structure similarity of low- and high-energy CT images, a global shortcut is used to ease model training. Clinical iodine contrast-enhanced dual energy CT image datasets of 19 patients are used in this study. The dual-energy scanning is performed by a SOMATOM Definition Flash DECT scanner. We split the datasets into training dataset of 15 patients, validation dataset of 1 patient, and testing dataset of 3 patients. The image size is 512 × 512 with pixel size 0.5 × 0.5 mm2.

RESULTS

The U-Net model with 1.95M parameters and 44.87G FLOPS achieved the averaged PSNR value of 44.55 dB (s.t.d. 1.34) and averaged RMSE value of 0.0060 (s.t.d. 0.001). In comparison, our efficient Transformer with 1.408M parameters and 31.375G FLOPS achieved the averaged PSNR value of 44.78 dB (s.t.d. 1.37) and RMSE value of 0.0059 (s.t.d. 0.001), demonstrating our model has better performance with small model size and less computation.

CONCLUSION

The efficient Transformer model allows high-resolution CT image synthesis with small model scale and computation burden from low-energy CT image.

The Role of CT-Based Radiomics Nomogram in Differential Diagnosis of Immune Checkpoint Inhibitor-Related Pneumonitis from Radiation Pneumonitis for Patients with ESCC

PURPOSE/OBJECTIVE(S)

The combination of immunotherapy and chemoradiotherapy has widely used for patients with esophageal squamous cell carcinoma (ESCC) and induced treatment-related adverse effects, particularly immune checkpoint inhibitor-related pneumonitis (CIP) and radiation pneumonitis (RP). The aim of this study is to differentiate between CIP and RP by the CT radiomics and clinical or radiological parameters.

MATERIALS/METHODS

A total of 76 ESCC patients with pneumonitis were enrolled in this retrospective study and divided into training dataset (n = 53) and validation dataset (n = 23). A total of 837 radiomics features were extracted from regions of interest (ROIs) based on the lung parenchyma window of CT images. A radiomics signature was constructed on the basis of the predictive features by the least absolute shrinkage and selection operator (LASSO). A logistic regression was applied to develop radiomics nomogram. Receiver operating characteristics (ROC) curve and area under the curve (AUC) were applied to evaluate the performance of pneumonitis etiology identification.

RESULTS

No significant difference was detected between training dataset and validation dataset. The radiomics signature which was made up of four radiomics features shown a favorable performance on differentiating between CIP and RP with the α-binormal-based and empirical AUC = 0.831 and 0.843. Patients with RP had a close relationship with location (p = 0.003) and shape of lesions (p = 0.002). The nomogram that combined with radiomics signature and clinical factors improved the classifying performance on discrimination in the training dataset (AUCαbin = 0.963 and AUCemp = 0.964). The results were verified in the validation dataset with AUC = 0.967 and 0.964.

CONCLUSION

CT-based radiomics features have potential values for differentiating between patients with CIP and RP. Addition of bilateral changes and sharp border produced superior model performance on classifying, which could be a useful method to improve related clinical decision-making.

Deep Learning-Based Single-View Fluorescence Dose Reconstruction for 3D Dosimetry

PURPOSE/OBJECTIVE(S)

3D dose distribution measurement is crucial for precise radiotherapy. Radiation-excited fluorescence imaging has potential for the 3D dosimetry with high spatial resolution, but multiple fluorescence images from different view-angles are required for analytical reconstruction techniques. Furthermore, the imaging data are contaminated by anisotropic Cherenkov light emission and statistical noise. This project aims to establish a novel deep learning-based model to predict 3D dose distributions from a single-view 2D fluorescence image while simultaneously removing the adverse effects of Cherenkov signals and other noises.

MATERIALS/METHODS

A total of 124 single-aperture static photon beams were delivered to an acrylic tank containing 1 g/L quinine hemisulfate water solution with varying aperture shapes and collimator angle. The emitted optical signals were detected by a low-cost CMOS camera for 20 seconds, and image pre-processing was performed to obtain input 2D fluorescence images with 0.3 × 0.3 mm spatial resolution. 3D back-projected dose distribution images were also calculated from the input fluorescence images. Ground-truth of 3D dose distributions and 2D field map images were obtained from a clinical treatment planning system with 1.4 × 1.4 × 1.4 mm spatial resolution. The proposed deep learning-based dose reconstruction method involved 3 steps. First, 2D fluence map images at the bottom plane of the tank were predicted from the fluorescence images by using a customized convolutional neural network (CNN). Second, the predicted fluence map images were transformed into the 2D field map images on the isocenter plane by applying perspective transformation. Finally, 2D dose distributions at a given radiological depth were calculated by using the predicted field map images, the back-projected dose distribution images, and the radiological depth value as inputs of a shallow CNN. Both CNN models were trained separately, and the 3D dose distributions were predicted by concatenating the output 2D dose distributions at various radiological depths.

RESULTS

The proposed CNN model yielded accurate 2D field map images. Averaged Dice similarity coefficient and mean absolute error of the field maps in the test data was 92.0% ± 4.6% and 0.0132 ± 0.0113, respectively. Moreover, our deep learning-based approach was able to predict accurate 3D dose distributions from the 2D fluorescence images. Mean squared error and averaged 3D gamma passing ratio (3%/3mm) were 9.55 mGy ± 6.8 mGy and 86.3% ± 9.86%, respectively.

CONCLUSION

Theproposed deep learning-based method calculated accurate 3D dose distributions from a single-view 2D fluorescence image. Since this technique require only a single CMOS camera image and fluorescent material, it can be readily used for any external radiotherapy modalities, including SRS/SBRT with small fields. This method is useful for acquiring 3D dose distribution data for precise dose verification within a few seconds.

Clinical Implementation of an Automated IMRT/VMAT Treatment Planning Tool

PURPOSE/OBJECTIVE(S)

To create an in-house automated treatment planning tool for IMRT/VMAT treatments and evaluate the dosimetric plan quality against manually generated plans.

MATERIALS/METHODS

A scripting application programming interface is employed to interact with a commercial treatment planning system (TPS) to implement automatic plan evaluation and update optimization parameters by mimicking the human planning process. The automated planning performs in an iterative fashion until reaching an acceptable tradeoff among target coverage/dose homogeneity and sparing of critical organs at risk. In each iteration, the dose constraints, priorities, and optimization structures for are automatically updated based on the results of the current iteration. Twenty previously treated plans (10 prostate and 10 head and neck), were preliminarily used to evaluate the performance of the automated planning tool. The differences in target and organ-at-risk metrics from the manually generated clinical plans were analyzed using paired t-test to evaluate clinical acceptability of tour automated planning tool. The current in-house-developed automated planning solution is able to create plans for different disease sites, including head & neck, prostate, pelvis, and lung. So far, the VMAT plans for more than 150 different cases have been generated with the tool. The results for these were also evaluated.

RESULTS

Compared to the manually generated clinical head and neck plans, all auto plans achieved PTV D95% coverage and critical organs at risk sparing without statistically significant change in average global Dmax (107.4% for manual vs 107.3% for automated plans). The auto-planning solution provided reduced maximum doses to brainstem and spinal cord (average reductions with standard deviations of 5.1 ± 2.6 Gy and 2.9 ± 1.4 Gy, respectively, all p <0.03), reduced average mean doses to contralateral parotid, ipsilateral parotid, contralateral submandibular gland, pharynx, esophagus, cochleae (reductions of 2.2 ± 2.9 Gy, 4.8 ± 4.7 Gy, 3.6 ± 5.2 Gy, 2.0 ± 7.1 Gy, 3.9 ± 2.6 Gy, 3.8 ± 5.0 Gy, respectively, all p < 0.045). Similar results were observed for the prostate plans. With the same PTV coverage and without statistically significant change in average global Dmax (106.5% for manual vs 106.8% for automated plans), the automated solution provided superior sparing for both bladder and rectum. Bladder V75, V70, V65 were reduced by 0.6% ± 2.1%, 0.8% ± 2.5%, and 0.9% ± 2.9% (all p <0.04), respectively. Rectum V75, V70, V65, V60 were reduced by 1.0% ± 2.3%, 1.2% ± 2.8%, 1.3% ± 3.2%, 1.6% ± 3.6% (all p < 0.01), respectively.

CONCLUSION

Our automated treatment planning solution is capable of efficiently generating VMAT plans for different disease sites with superior dosimetric indices compared to manually generated plans. Our tool is integrated within a commercial TPS platform, so it has the advantage of seamless adoption into the standard workflow to improve plan quality and treatment planning efficiency in our clinic.